Space planning processing system based on GIS

Abstract

The invention belongs to the technical field of space planning, and particularly relates to a GIS-based space planning processing system, which comprises a GIS model construction module, an optimal to-be-developed sub-region determination module and a space planning scheme acquisition module, and is characterized in that the GIS model construction module is used for acquiring to-be-preprocessed data of a current to-be-planned processing region; preprocessing the to-be-preprocessed data to obtain preprocessed data, and constructing a GIS model based on the preprocessed data; the optimal to-be-developed sub-region determination module is used for dividing a plurality of to-be-developed sub-regions and determining an optimal to-be-developed sub-region; and the space planning scheme acquisition module is used for obtaining a land type classification result image of the optimal to-be-developed sub-region, generating a thermodynamic diagram from the land type classification result image of the optimal to-be-developed sub-region through a GIS (Geographic Information System), and acquiring a space planning scheme. According to the method, various key elements of regional development and mutual relations thereof can be comprehensively considered, and a planning scheme for balancing and coordinating interests and demands in various aspects is formulated.

Description

Technical Field

[0001] The invention belongs to the technical field of space planning, and in particular relates to a space planning processing system based on GIS. Background Art

[0002] GIS (Geographic Information System) is an integrated technology and system that can capture, store, analyze and manage geographic data. GIS manages various types of data, such as topography, land use and transportation networks, through a layer system, allowing users to perform complex spatial analysis and decision support. GIS combines the power of mapping with data analysis, allowing users to extract information from spatial relationships and present it in a visual way.

[0003] GIS technology has played an important role in national land space planning, but there are still the following shortcomings: insufficient consistency in data integration. When processing multi-source data, it is difficult to ensure the consistency of data integration, which may lead to inaccurate analysis results; lack of scientificity in regional screening. When determining the areas to be developed, the existing methods lack a scientific regional screening mechanism, which may lead to irrational resource allocation; waste of resources. Due to unscientific regional screening, resources may be wasted, affecting the efficiency and effectiveness of planning; the accuracy of analysis results and decision-making are affected. The above problems ultimately affect the accuracy of analysis results and the scientificity of decision-making, thereby affecting the overall quality of national land space planning. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a GIS-based spatial planning processing system that can comprehensively consider various key factors of regional development and their interrelationships, and formulate a planning scheme that balances and coordinates the interests and needs of all parties.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A spatial planning processing system based on GIS includes a GIS model building module, an optimal sub-region to be developed determination module and a spatial planning scheme acquisition module, wherein: GIS model building module, used to obtain the data to be preprocessed in the current area to be planned and processed, and preprocess the data to be preprocessed to obtain preprocessed data, the data to be preprocessed includes terrain data, land use data, population distribution data, traffic data and environmental data, and the preprocessed data includes preprocessed terrain data, preprocessed land use data, preprocessed population distribution data, preprocessed traffic data and preprocessed environmental data; Build GIS model based on preprocessed data; The module for determining the best sub-region to be developed is used to analyze the current area to be planned and processed through the GIS model, divide it into multiple sub-regions to be developed, and determine the best sub-region to be developed; The spatial planning scheme acquisition module is used to obtain the image of the best sub-area to be developed, identify different land use types, obtain the land type classification result image of the best sub-area to be developed, generate a heat map from the land type classification result image of the best sub-area to be developed through GIS, and obtain the spatial planning scheme.

[0006] Preferably, constructing a GIS model based on the preprocessed data comprises the following steps: Extracting features from the preprocessed data to obtain feature data, including terrain feature data, land use feature data, population distribution feature data, traffic feature data, and environmental feature data; Integrate the extracted feature data into the ArcGIS geographic database, and use ArcGIS's spatial registration tool to make the terrain feature data, land use feature data, population distribution feature data, traffic feature data, and environmental feature data correspond in spatial position; Use ArcGIS overlay analysis tools to conduct spatial relationship analysis, and use spatial statistical analysis tools to conduct quantitative relationship analysis; Based on the results of spatial relationship analysis and quantitative relationship analysis, ModelBuilder is used in ArcGIS to build a GIS model.

[0007] Preferably, the current area to be planned and processed is analyzed by a GIS model to divide a plurality of sub-areas to be developed, including the following steps: Based on the acquired characteristic data of the current pre-processed area to be planned, the characteristic data is compared with the case characteristic data of each case area stored in the database to obtain the comprehensive similarity of each area, and the case characteristic data includes case terrain characteristic data, case land use characteristic data, case population distribution characteristic data, case traffic characteristic data and case environment characteristic data; Arrange the comprehensive similarities of each region from small to large, and determine the largest comprehensive similarity of the region: Determine case feature data of the corresponding case area based on the maximum regional comprehensive similarity, and obtain the regional division standard corresponding to the case feature data of the case area from the database; Based on the determined regional division standards, multiple sub-areas to be developed are divided out.

[0008] Preferably, obtaining the comprehensive similarity of each region includes the following steps: Compare the terrain feature data with the case terrain feature data of each case area to obtain the terrain similarity of each area; The land use characteristic data are compared with the case land use characteristic data of each case area to obtain the similarity of each land use; The population distribution characteristic data are compared with the case population distribution characteristic data of each case area to obtain the similarity of each population distribution; The traffic characteristic data are compared with the case traffic characteristic data of each case area to obtain the traffic similarity of each case; Compare the environmental feature data with the case environmental feature data of each case area to obtain the similarity of each environment; The terrain similarity, land use similarity, population distribution similarity, traffic similarity and environmental similarity corresponding to each case area are comprehensively analyzed to obtain the comprehensive similarity of each area.

[0009] Preferably, determining the best sub-region to be developed includes the following steps: Obtaining terrain data of each sub-area to be developed, and obtaining a terrain assessment value of each sub-area to be developed, wherein the terrain data includes slope, terrain undulation, and elevation; Obtaining the land use data of each sub-region to be developed, and obtaining the land use assessment value of each sub-region to be developed, the land use data includes building density, cultivated land utilization rate, and green area; Obtain the population distribution data of each sub-region to be developed, and obtain the population distribution evaluation value of each sub-region to be developed. The population distribution data includes employment density, the ratio of registered population to floating population, and population density; Obtaining the traffic data of each sub-region to be developed and obtaining the traffic assessment value of each sub-region to be developed, the traffic data includes road network density and traffic flow; Obtain environmental data of each sub-region to be developed and obtain environmental assessment values ​​of each sub-region to be developed. Environmental data include air quality index, green land coverage rate, and precipitation; Comprehensively analyze the terrain assessment value, land use assessment value, population distribution assessment value, traffic assessment value, and environmental assessment value corresponding to each sub-region to be developed to obtain a comprehensive assessment value for each sub-region to be developed; The sub-regions to be developed are sorted from large to small based on their comprehensive evaluation values. The sub-region to be developed corresponding to the largest comprehensive evaluation value of the sub-region to be developed is the best sub-region to be developed.

[0010] Preferably, the calculation formula of the comprehensive evaluation value is: ; Where A is the comprehensive evaluation value of the i-th sub-region to be developed, is the terrain assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of is the land use assessment value of the i-th sub-region to be developed, Stored in the database The weight factor of is the population distribution assessment value of the i-th sub-region to be developed, Stored in the database The weight factor of is the traffic assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of is the environmental assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of , i is the number of the sub-area to be developed.

[0011] Preferably, acquiring an image of the best sub-region to be developed, identifying different land use types, and obtaining a land type classification result image of the best sub-region to be developed includes the following steps: Based on the map output function of GIS software, the geospatial data of the best sub-region to be developed is output into an image format to obtain an image of the best sub-region to be developed; Performing grid division on the image of the best sub-region to be developed to obtain grid units; Conduct land type analysis on each grid unit to determine the land type corresponding to each grid unit; If the land type of a certain grid cell is the same as the land type of its adjacent grid cell, the two grid cells are merged to obtain the land type classification result image of the best sub-area to be developed.

[0012] Preferably, determining the land type corresponding to each grid unit includes the following steps: Obtaining spectral feature data and texture feature data of the grid unit; Obtaining spectral feature parameter data and texture feature parameter data corresponding to each land type stored in the database; Compare the spectral feature data with the spectral feature parameter data corresponding to each land type to obtain the similarity of each spectrum; Compare the texture feature data with the texture feature parameter data corresponding to each land type to obtain the similarity of each texture; The spectral similarity and texture similarity corresponding to each land type are comprehensively analyzed to obtain the matching similarity of each land type; The land type corresponding to the largest land type matching similarity is the land type corresponding to the grid unit.

[0013] Preferably, obtaining a spatial planning solution comprises the following steps: Based on the thermal map of the best sub-area to be developed, key information data of the best sub-area to be developed are obtained, including heat distribution data, land type ratio data, and heat gradient data; Compare the key information data with the reference key information data of each reference sub-region to be developed stored in the database to obtain the similarity of information of each region; Reference key information data include reference heat distribution data, reference land type ratio data, and reference heat gradient data; Determine the maximum regional information similarity, and record the reference key information data corresponding to the maximum regional information similarity as the reference key information data closest to the key information data; A corresponding reference space planning scheme is obtained from a database based on the reference key information data that is closest to the key information data.

[0014] Preferably, obtaining the similarity of information of each region includes the following steps: Compare the heat distribution data with the reference heat distribution data of each reference sub-region to be developed to obtain the similarity of each heat distribution; Compare the land type ratio data with the reference land type ratio data of each reference sub-area to be developed to obtain the similarity of each land type ratio; Compare the heat gradient data with the reference heat gradient data of each reference sub-region to be developed to obtain the similarity of each heat gradient; The similarity of heat distribution, land type proportion and heat gradient corresponding to each reference sub-region to be developed is comprehensively analyzed to obtain the information similarity of each region.

[0015] The present invention has the following beneficial effects: The present invention can comprehensively consider various key factors of regional development and their interrelationships, formulate a planning scheme that balances and coordinates the interests and needs of all parties, avoids development imbalance or waste of resources due to one-sided focus on a certain factor, screens out the regions with the most comprehensive advantages, guides resources to concentrate in the regions with the most potential and advantages, realizes efficient development and utilization, improves the rationality and effectiveness of resource allocation, promotes coordinated and balanced development of the entire area to be planned, facilitates centralized planning and optimization of industrial layout, reduces mutual interference between different industrial lands, improves the pertinence and effectiveness of infrastructure supporting facilities, promotes industrial collaborative development and resource sharing, and enhances the overall development benefits of the region. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a module diagram of a GIS-based spatial planning processing system of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention are described clearly and completely below in conjunction with the accompanying drawings.

[0018] like Figure 1As shown, a spatial planning processing system based on GIS includes a GIS model building module, an optimal sub-area determination module for development, and a spatial planning scheme acquisition module, wherein: The GIS model building module is used to obtain the data to be preprocessed in the current area to be planned and processed, and preprocess the data to be preprocessed to obtain preprocessed data. The data to be preprocessed include terrain data, land use data, population distribution data, traffic data and environmental data. The preprocessed data include preprocessed terrain data, preprocessed land use data, preprocessed population distribution data, preprocessed traffic data and preprocessed environmental data; and build a GIS model based on the preprocessed data.

[0019] The GIS model building module preprocesses the data to be preprocessed in the planning processing area in the following steps: for terrain data, first convert the format and check the integrity, correct the coordinate system, deal with outliers, and then interpolate and rasterize; land use data must unify the classification standards, check the supplementary integrity, build topological relationships and correct errors; population distribution data must be integrated, deduplicated, spatially processed, and verified and corrected; traffic data must be encoded in a unified format, a topological network must be built, and flow data must be cleaned and sorted; environmental data must be in a unified format and aligned in time and space, outliers must be detected and corrected, and standardized and normalized. These preprocessed data will lay a good foundation for subsequent planning analysis.

[0020] Perform feature extraction on the preprocessed data to obtain feature data, including terrain feature data, land use feature data, population distribution feature data, traffic feature data and environmental feature data; integrate the extracted feature data into an ArcGIS geographic database, and use ArcGIS's spatial registration tool to make the terrain feature data, land use feature data, population distribution feature data, traffic feature data and environmental feature data correspond in spatial position; use ArcGIS's overlay analysis tool to perform spatial relationship analysis, and use spatial statistical analysis tools to perform quantitative relationship analysis; based on the results of spatial relationship analysis and quantitative relationship analysis, use ModelBuilder in ArcGIS to build a GIS model.

[0021] Different types of feature data (topography, land use, population distribution, transportation, environment) are integrated into the ArcGIS geographic database, and spatial registration tools are used to make them correspond in spatial position, thus realizing the effective integration and spatial calibration of multi-source data. Using ArcGIS's overlay analysis tools to perform spatial relationship analysis on various integrated and registered feature data can reveal the spatial relationships between different data layers, such as intersection, inclusion, and adjacency. Using spatial statistical analysis tools to perform quantitative relationship analysis on feature data can uncover the quantitative relationships hidden behind the data, such as the quantitative association between population distribution and land use types in different regions, and the quantitative relationship between traffic flow and environmental quality. Based on the results of spatial relationship analysis and quantitative relationship analysis, a GIS model is constructed to integrate complex geographic analysis processes and data relationships into a reusable and automated model framework.

[0022] In the process of GIS model construction, in terms of data integration and spatial consistency, multi-source feature data are integrated into the ArcGIS geographic database and aligned, which has many advantages: unified management and accurate spatial analysis can be ensured; spatial correlations between data layers can be revealed, such as helping to define the boundaries of protected areas in ecological planning and achieving efficient resource utilization in land use planning; it can provide a basis for population forecasts in socio-economic research and provide accurate data for policy making in environmental science, which can improve the efficiency and accuracy of scientific research and planning work, reduce duplication of work and errors, and effectively promote the widespread application of geographic information science in many fields.

[0023] The module for determining the best sub-region to be developed is used to analyze the current area to be planned and processed through the GIS model, divide it into multiple sub-regions to be developed, and determine the best sub-region to be developed.

[0024] Based on the acquired characteristic data of the current pre-processed area to be planned, the characteristic data is compared with the case characteristic data of each case area stored in the database to obtain the comprehensive similarity of each area; the case characteristic data includes case terrain characteristic data, case land use characteristic data, case population distribution characteristic data, case traffic characteristic data and case environment characteristic data; the comprehensive similarities of each area are arranged in order from small to large, and the largest regional comprehensive similarity is determined: based on the largest regional comprehensive similarity, the case characteristic data of the corresponding case area is determined, and the regional division standard corresponding to the case characteristic data of the case area is obtained from the database; based on the determined regional division standard, a plurality of sub-areas to be developed are divided.

[0025] The terrain feature data are compared with the case terrain feature data of each case area to obtain the terrain similarities of each area; the land use feature data are compared with the case land use feature data of each case area to obtain the land use similarities of each area; the population distribution feature data are compared with the case population distribution feature data of each case area to obtain the population distribution similarities of each area; the traffic feature data are compared with the case traffic feature data of each case area to obtain the traffic similarities of each area; the environmental feature data are compared with the case environmental feature data of each case area to obtain the environmental similarities of each area; the terrain similarity, land use similarity, population distribution similarity, traffic similarity and environmental similarity corresponding to each case area are comprehensively analyzed to obtain the comprehensive similarity of each area.

[0026] Terrain similarity, land use similarity, population distribution similarity, traffic similarity and environment similarity are all calculated using cosine similarity. For example, the calculation formula for terrain similarity is: ; In the formula, is the terrain similarity between the current pre-processed area to be planned and the jth case area, is the terrain feature vector representation of the current pre-processed area to be planned, used to represent terrain feature data. is the case terrain feature vector representation of the jth case area stored in the database, used to represent the case terrain feature data of the case area, for The model, for The module of , j is the number of each case area stored in the database.

[0027] The calculation formula of regional comprehensive similarity is: ; In the formula, is the comprehensive similarity between the current pre-processed area to be planned and the jth case area. Stored in the database The weight factor of Stored in the database The weight factor of Stored in the database The weight factor of Stored in the database The weight factor of Stored in the database The weight factor of .

[0028] The various weight factors in this embodiment are obtained from a mapping set established in a database based on historical data.

[0029] The terrain similarity calculation formula accurately quantifies the similarity between the current pre-processed area to be planned and the case area in terms of terrain by calculating the ratio of the inner product of the terrain feature vectors of the two areas to the product of their moduli. It can well measure the terrain similarity, get rid of the vague qualitative description of the terrain, determine the similarity between the current area and the case area in terms of land use type, structure and layout, and provide a quantitative basis for land use planning. The population distribution similarity calculation formula measures the similarity between the two areas in terms of population size, density and distribution pattern. By comparing with the case area with similar population distribution, the demand scale and layout location can be accurately estimated to avoid excessive or insufficient concentration of facilities and ensure that residents can enjoy public services conveniently.

[0030] The traffic similarity calculation formula quantitatively compares the traffic characteristics of the two regions, such as the traffic network structure, line density, station distribution, and traffic flow, which is helpful for traffic planning and optimization. Referring to the traffic improvement measures in the case area with high traffic similarity, it is possible to improve traffic operation efficiency and alleviate congestion problems. The environmental similarity calculation formula evaluates the similarity between the current area and the case area in terms of environmental characteristics such as environmental quality, ecosystem structure, and pollution status, and plays a key role in environmental protection and ecological construction planning. In application, similar cases can be found based on environmental similarity, and their sewage treatment, ecological restoration and other measures can be used as reference to achieve sustainable improvement of the regional environment.

[0031] By comprehensively comparing the characteristic data of the current area to be planned with the characteristic data of many case areas in the database, the sub-areas to be developed can be accurately divided according to the comprehensive similarity of the regions, making the division results more objective and scientific. With the help of comparative analysis with the case areas, we can fully learn from the lessons learned in the past regional development process. There is no need to explore from scratch, and you can directly refer to the effective models of similar areas in terrain utilization, land development, population planning, transportation layout and environmental management, thereby saving a lot of research and planning costs, accelerating the planning process of the current region and improving the planning quality.

[0032] Obtain the terrain data of each sub-area to be developed, and obtain the terrain assessment value of each sub-area to be developed. The terrain data include slope, terrain undulation, and elevation; obtain the land use data of each sub-area to be developed, and obtain the land use assessment value of each sub-area to be developed. The land use data include building density, cultivated land utilization rate, and green space area; obtain the population distribution data of each sub-area to be developed, and obtain the population distribution assessment value of each sub-area to be developed. The population distribution data include employment density, the ratio of registered population to floating population, and population density; obtain the traffic data of each sub-area to be developed, and obtain the traffic assessment value of each sub-area to be developed. The traffic data include road network density and traffic flow.

[0033] Obtain environmental data of each sub-region to be developed and obtain environmental assessment values ​​of each sub-region to be developed, the environmental data including air quality index, green coverage rate and precipitation; conduct comprehensive analysis on terrain assessment values, land use assessment values, population distribution assessment values, traffic assessment values ​​and environmental assessment values ​​corresponding to each sub-region to be developed to obtain comprehensive assessment values ​​of each sub-region to be developed; sort the sub-regions to be developed from large to small based on their comprehensive assessment values, and the sub-region to be developed corresponding to the sub-region with the largest comprehensive assessment value is the best sub-region to be developed.

[0034] The calculation formula for the comprehensive evaluation value is: ; Where A is the comprehensive evaluation value of the i-th sub-region to be developed, is the terrain assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of is the land use assessment value of the i-th sub-region to be developed, Stored in the database The weight factor of is the population distribution assessment value of the i-th sub-region to be developed, Stored in the database The weight factor of is the traffic assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of is the environmental assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of , i is the number of the sub-area to be developed.

[0035] Among them, the terrain assessment value The calculation formula is: ; In the formula, is the slope of the i-th sub-area to be developed, is the terrain relief of the i-th sub-area to be developed, is the elevation of the ith sub-area to be developed, Pd is the reference slope, Qf is the reference terrain undulation, and Gc is the reference elevation.

[0036] Land use assessment value The calculation formula is: ; In the formula, is the building density of the i-th sub-area to be developed, is the cultivated land utilization rate of the i-th sub-region to be developed, is the green area of ​​the ith sub-area to be developed, Jm is the reference building density, Gl is the reference cultivated land utilization rate, and Lm is the reference green area.

[0037] Population distribution assessment The calculation formula is: ; In the formula, is the employment density of the i-th sub-region to be developed, is the ratio of registered population to floating population in the i-th sub-region to be developed, is the population density of the ith sub-region to be developed, Jy is the reference employment density, Ld is the reference ratio of registered population to floating population, and Rk is the reference population density.

[0038] Traffic Assessment Value The calculation formula is: ; In the formula, is the road network density of the i-th sub-region to be developed, is the traffic flow of the i-th sub-area to be developed, Dw is the reference road network density, and Jl is the reference traffic flow.

[0039] Environmental assessment value The calculation formula is: ; In the formula, is the air quality index of the i-th sub-region to be developed, is the green coverage rate of the i-th sub-area to be developed, is the precipitation of the ith sub-area to be developed, Kz is the reference air quality index, Lf is the reference green space coverage rate, and Js is the reference precipitation.

[0040] Topographic relief is a comprehensive indicator that describes the topographic changes in a region, reflecting the height difference between the highest and lowest points in the region. Elevation has a key impact on the climate, ecosystem, and some highly sensitive construction projects (such as airports, communication base stations, etc.) in the sub-region to be developed.

[0041] Building density refers to the ratio of the total base area of ​​buildings in the sub-region to be developed to the construction land area, reflecting the compactness of land use. The utilization rate of cultivated land reflects the efficiency of land resources in agriculture. Green area refers to the total area of ​​green land in the sub-region to be developed, including parks, street green spaces, protective green spaces, etc. Sufficient green area helps to improve the living quality and ecological sustainability of the region, and is also one of the important indicators to measure whether the land use of a region is reasonable.

[0042] Road network density refers to the ratio of the total length of roads in the sub-region to be developed to the area of ​​the region. It is an important indicator to measure the degree of perfection of regional transportation infrastructure.

[0043] The air quality index is a dimensionless index that quantitatively describes the air quality status and can intuitively reflect the atmospheric environment quality of the sub-region to be developed. For the sub-region to be developed, precipitation is an important factor affecting the regional water resources status, ecosystem, and the development of industries such as agriculture.

[0044] The terrain evaluation value calculation formula can reflect the relative quality of regional terrain conditions and can quickly and accurately evaluate the terrain suitability of different sub-regions to be developed. The population distribution evaluation value calculation formula can reflect the impact of regional population distribution characteristics on development. It can help determine which sub-regions to be developed are more suitable for the layout of commercial facilities or industrial parks to meet population needs and promote economic development. The traffic evaluation value calculation formula can evaluate the quality of regional traffic conditions and can intuitively reflect the degree of perfection of regional transportation infrastructure and traffic congestion.

[0045] The environmental assessment value calculation formula can measure the regional environmental quality and ecological status, and clearly show the environmental quality of each sub-region to be developed. By obtaining and comprehensively analyzing the data on topography, land use, population distribution, transportation, environment and other aspects of the sub-region to be developed, we can comprehensively consider the various conditions of each sub-region, select the regions with the most comprehensive advantages for key investment and development, improve the rationality and effectiveness of resource allocation, guide resources to concentrate in the regions with the most potential and advantages, and achieve efficient development and utilization. At the same time, for regions with relatively low comprehensive assessment values, targeted improvements and optimizations can also be made based on the weak links in their assessment results to promote the coordinated and balanced development of the entire area to be planned and processed.

[0046] The spatial planning scheme acquisition module is used to obtain the image of the best sub-area to be developed, identify different land use types, obtain the land type classification result image of the best sub-area to be developed, generate a heat map from the land type classification result image of the best sub-area to be developed through GIS, and obtain the spatial planning scheme.

[0047] Based on the map output function of GIS software, the geographic spatial data of the best sub-region to be developed is output into an image format to obtain an image of the best sub-region to be developed; the image of the best sub-region to be developed is gridded to obtain grid cells; the land type of each grid cell is analyzed to determine the land type corresponding to each grid cell; if the land type of a grid cell is the same as the land type of its adjacent grid cell, the two grid cells are merged to obtain the land type classification result image of the best sub-region to be developed.

[0048] Acquire the spectral feature data and texture feature data of the grid unit; acquire the spectral feature parameter data and texture feature parameter data corresponding to each land type stored in the database; compare the spectral feature data with the spectral feature parameter data corresponding to each land type to obtain each spectral similarity; compare the texture feature data with the texture feature parameter data corresponding to each land type to obtain each texture similarity; comprehensively analyze the spectral similarity and texture similarity corresponding to each land type to obtain the matching similarity of each land type; the land type corresponding to the largest land type matching similarity is the land type corresponding to the grid unit.

[0049] The spectral similarity and texture similarity may be obtained by methods including but not limited to the inverse of the Euclidean distance value plus 1, the Pearson correlation coefficient, and the cosine similarity.

[0050] The calculation formula for land type matching similarity is: ; In the formula, is the land type matching similarity between the ath grid cell in the image of the best sub-area to be developed and the bth land type, Stored in the database The weight factor of Stored in the database The weight factor is , a is the grid cell number in the image of the best sub-area to be developed, and b is the land type number.

[0051] In urban construction planning, accurate land type classification results can clearly show the layout relationship between the scope of land available for commercial development and other surrounding land types, avoiding confusion in functional layout or waste of land resources due to unclear land types during planning. And through heat maps, the overall characteristics of the region can be grasped in a short time, so as to more efficiently formulate spatial planning plans that meet regional characteristics and development needs, and improve decision-making efficiency.

[0052] Based on the heat map of the best sub-region to be developed, key information data of the best sub-region to be developed is obtained, and the key information data includes heat distribution data, land type ratio data, and heat gradient data; the key information data is compared with the reference key information data of each reference sub-region to be developed stored in the database to obtain the information similarity of each region; the reference key information data includes reference heat distribution data, reference land type ratio data, and reference heat gradient data; the maximum regional information similarity is determined, and the reference key information data corresponding to the maximum regional information similarity is recorded as the reference key information data closest to the key information data; based on the reference key information data closest to the key information data, the corresponding reference space planning scheme is obtained from the database.

[0053] Compare the heat distribution data with the reference heat distribution data of each reference sub-region to be developed to obtain the similarity of each heat distribution; compare the land type ratio data with the reference land type ratio data of each reference sub-region to be developed to obtain the similarity of each land type ratio; compare the heat gradient data with the reference heat gradient data of each reference sub-region to be developed to obtain the similarity of each heat gradient; conduct a comprehensive analysis of the heat distribution similarity, land type ratio similarity and heat gradient similarity corresponding to each reference sub-region to be developed to obtain the information similarity of each region. The land type ratio data is calculated by combining the land type classification result image to calculate the area ratio of different land types in different heat regions.

[0054] For example, in a high-heat area, what proportion of construction land, agricultural land, water area, etc. can be obtained by statistically analyzing the area of ​​spatial units (based on vector data) or pixels (based on raster data) of different land types in GIS software, and classifying and summarizing them according to the range of heat values, which can reflect the land use structure characteristics of different heat areas.

[0055] The calculation formula of regional comprehensive similarity is: ; In the formula, is the comprehensive similarity of the cth region, is the cth heat distribution similarity, is the similarity of the cth land type ratio, is the cth thermal gradient similarity, Stored in the database The weight factor of Stored in the database The weight factor of Stored in the database The weight factor of , c is the number of the reference sub-area to be developed.

[0056] Among them, the heat distribution similarity and heat gradient similarity are both obtained using the cosine similarity algorithm, and the land type ratio similarity can be obtained using the inverse of the Euclidean distance value plus 1.

[0057] The regional comprehensive similarity calculation formula weights and sums the heat distribution similarity, land type ratio similarity and heat gradient similarity according to the weight factors stored in the database, and comprehensively evaluates the matching degree between the grid unit and the specific land type in multiple aspects. It provides a scientific basis for the formulation of targeted protection measures and reasonable development and utilization planning, and ensures the integrity and sustainability of the regional ecosystem.

[0058] By comparing the key information data of the best sub-region to be developed with the reference key information data of the reference sub-region to be developed, we can find areas that are similar to the best sub-region to be developed in terms of heat distribution, land type ratio and heat gradient, and draw on their corresponding spatial planning schemes. This planning scheme selection method based on data similarity improves the scientificity and rationality of spatial planning, reduces planning errors and waste of resources. When comparing multiple reference sub-regions to be developed, it is possible to more accurately determine which area is most similar to the best sub-region to be developed, thereby more accurately selecting the appropriate reference spatial planning scheme.

[0059] A specific example is used to illustrate the calculation of the formula in this implementation: when the feature data of the current pre-processing area to be planned is compared with the case feature data of the jth case area, To simplify the calculation, we get is 0.86, To simplify the calculation, we get is 0.71, To simplify the calculation, we get is 0.77, To simplify the calculation, we get is 0.96, To simplify the calculation, we get is 0.89, is 0.25, is 0.2, is 0.2, is 0.15, is 0.2, and the calculated The regional comprehensive similarity of the largest case area is 0.92. The regional division standard corresponding to the case feature data of the corresponding case area is the regional division standard based on the current pre-processed area to be planned. Based on this regional division standard, multiple sub-areas to be developed are divided.

[0060] In the terrain data, the i-th sub-area to be developed is 15°, 30m, 430m, is 14°, Qf is 25m, Gc is 450m, and the calculated is 0.27.

[0061] In the land use data, the i-th sub-area to be developed is 40%, is 70%, is 2000, Jm is 45%, Gl is 65%, Lm is 2500, and the calculated is 1.07.

[0062] In the population distribution data, the number of 270 people / hectare, is 1.7, =875 people / km2, Jy = 300 people / hectare, Ld = 1.8, Rk = 913 people / km2, It is 0.84.

[0063] In the traffic data, the i-th sub-area to be developed 5.4km / km 2 , is 338 vehicles / hour, Dw is 6km / km2, Jl is 460 vehicles / hour, and the calculated is 0.09.

[0064] In the environmental data, In the sub-areas to be developed is 73, is 42%, is 670mm, Kz is 55, Lf is 55%, Js is 730mm, and the calculated is 3.05, is 0.2, is 0.25, is 0.2, is 0.15, is 0.2, and the calculated The value of the largest sub-region to be developed is 5.35, and the corresponding sub-region to be developed is the best sub-region to be developed.

[0065] When comparing the spectral feature parameter data and texture feature parameter data corresponding to the ath grid cell in the land type classification with the bth land type, To simplify the calculation, we get is 4.23, To simplify the calculation, we get is 5.35, is 0.5, is 0.5, and the calculated The maximum land type matching similarity is 4.79, and the similarity calculation of other land types is 5.67, and the corresponding land type is the land type corresponding to the grid unit.

[0066] When the heat distribution data is compared with the reference heat distribution data of the cth reference sub-area to be developed, To simplify the calculation, we get is 0.88, To simplify the calculation, we get is 0.93, To simplify the calculation, we get is 0.85, is 0.3, is 0.4, is 0.3, calculated The regional information similarity of other reference sub-regions to be developed is 0.89, and the maximum regional information similarity obtained is 0.93. The corresponding reference space planning scheme is the reference space planning scheme corresponding to the best sub-region to be developed.

[0067] The device for implementing the above calculation includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The above calculation process is implemented by executing the computer program through the processor.

Claims

1. A spatial planning processing system based on GIS, characterized in that: It includes GIS model building module, optimal sub-area determination module and spatial planning scheme acquisition module, among which: GIS model building module, used to obtain the data to be preprocessed in the current area to be planned and processed, and preprocess the data to be preprocessed to obtain preprocessed data, the data to be preprocessed includes terrain data, land use data, population distribution data, traffic data and environmental data, and the preprocessed data includes preprocessed terrain data, preprocessed land use data, preprocessed population distribution data, preprocessed traffic data and preprocessed environmental data; Build GIS model based on preprocessed data; The module for determining the best sub-region to be developed is used to analyze the current area to be planned and processed through the GIS model, divide it into multiple sub-regions to be developed, and determine the best sub-region to be developed; The spatial planning scheme acquisition module is used to obtain the image of the best sub-area to be developed, identify different land use types, obtain the land type classification result image of the best sub-area to be developed, generate a heat map from the land type classification result image of the best sub-area to be developed through GIS, and obtain the spatial planning scheme.

2. A GIS-based spatial planning processing system according to claim 1, characterized in that: Building a GIS model based on preprocessed data includes the following steps: Extract features from the preprocessed data to obtain feature data, including terrain feature data, land use feature data, population distribution feature data, traffic feature data, and environmental feature data; Integrate the extracted feature data into the ArcGIS geographic database, and use ArcGIS's spatial registration tool to make the terrain feature data, land use feature data, population distribution feature data, traffic feature data, and environmental feature data correspond in spatial position; Use ArcGIS overlay analysis tools to conduct spatial relationship analysis, and use spatial statistical analysis tools to conduct quantitative relationship analysis; Based on the results of spatial relationship analysis and quantitative relationship analysis, ModelBuilder is used in ArcGIS to build a GIS model.

3. The GIS-based spatial planning processing system according to claim 1, characterized in that: The current area to be planned and processed is analyzed through the GIS model, and multiple sub-areas to be developed are divided, including the following steps: Based on the acquired characteristic data of the current pre-processed area to be planned, the characteristic data is compared with the case characteristic data of each case area stored in the database to obtain the comprehensive similarity of each area, and the case characteristic data includes case terrain characteristic data, case land use characteristic data, case population distribution characteristic data, case traffic characteristic data and case environment characteristic data; Arrange the comprehensive similarities of each region from small to large, and determine the largest comprehensive similarity of the region: Determine case feature data of the corresponding case area based on the maximum regional comprehensive similarity, and obtain the regional division standard corresponding to the case feature data of the case area from the database; Based on the determined regional division standards, multiple sub-areas to be developed are divided out.

4. A GIS-based spatial planning processing system according to claim 3, characterized in that: Obtaining the comprehensive similarity of each region includes the following steps: Compare the terrain feature data with the case terrain feature data of each case area to obtain the terrain similarity of each area; The land use characteristic data are compared with the case land use characteristic data of each case area to obtain the similarity of each land use; The population distribution characteristic data are compared with the case population distribution characteristic data of each case area to obtain the similarity of each population distribution; The traffic characteristic data are compared with the case traffic characteristic data of each case area to obtain the traffic similarity of each case; Compare the environmental feature data with the case environmental feature data of each case area to obtain the similarity of each environment; The terrain similarity, land use similarity, population distribution similarity, traffic similarity and environmental similarity corresponding to each case area are comprehensively analyzed to obtain the comprehensive similarity of each area.

5. The GIS-based spatial planning processing system according to claim 1, characterized in that: Determine the best sub-area to be developed, including the following steps: Obtaining terrain data of each sub-area to be developed, and obtaining a terrain assessment value of each sub-area to be developed, wherein the terrain data includes slope, terrain undulation, and elevation; Obtaining the land use data of each sub-region to be developed, and obtaining the land use assessment value of each sub-region to be developed, the land use data includes building density, cultivated land utilization rate, and green area; Obtain the population distribution data of each sub-region to be developed, and obtain the population distribution evaluation value of each sub-region to be developed. The population distribution data includes employment density, the ratio of registered population to floating population, and population density; Obtaining the traffic data of each sub-region to be developed and obtaining the traffic assessment value of each sub-region to be developed, the traffic data includes road network density and traffic flow; Obtain environmental data of each sub-region to be developed and obtain environmental assessment values ​​of each sub-region to be developed. Environmental data include air quality index, green land coverage rate, and precipitation; Comprehensively analyze the terrain assessment value, land use assessment value, population distribution assessment value, traffic assessment value, and environmental assessment value corresponding to each sub-region to be developed to obtain a comprehensive assessment value for each sub-region to be developed; The sub-regions to be developed are sorted from large to small based on their comprehensive evaluation values. The sub-region to be developed corresponding to the largest comprehensive evaluation value of the sub-region to be developed is the best sub-region to be developed.

6. A GIS-based spatial planning processing system according to claim 5, characterized in that: The calculation formula for the comprehensive evaluation value is: ； Where A is the comprehensive evaluation value of the i-th sub-region to be developed, is the terrain assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of is the land use assessment value of the i-th sub-region to be developed, Stored in the database The weight factor of is the population distribution assessment value of the i-th sub-region to be developed, Stored in the database The weight factor of is the traffic assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of is the environmental assessment value of the i-th sub-area to be developed, Stored in the database The weight factor of , i is the number of the sub-area to be developed.

7. The GIS-based spatial planning processing system according to claim 1, characterized in that: Acquiring an image of the best sub-region to be developed, identifying different land use types, and obtaining a land type classification result image of the best sub-region to be developed includes the following steps: Based on the map output function of GIS software, the geospatial data of the best sub-region to be developed is output into an image format to obtain an image of the best sub-region to be developed; Performing grid division on the image of the best sub-region to be developed to obtain grid units; Conduct land type analysis on each grid unit to determine the land type corresponding to each grid unit; If the land type of a certain grid cell is the same as the land type of its adjacent grid cell, the two grid cells are merged to obtain the land type classification result image of the best sub-area to be developed.

8. A GIS-based spatial planning processing system according to claim 7, characterized in that: Determining the land type corresponding to each grid cell includes the following steps: Obtaining spectral feature data and texture feature data of the grid unit; Obtaining spectral feature parameter data and texture feature parameter data corresponding to each land type stored in the database; Compare the spectral feature data with the spectral feature parameter data corresponding to each land type to obtain the similarity of each spectrum; Compare the texture feature data with the texture feature parameter data corresponding to each land type to obtain the similarity of each texture; The spectral similarity and texture similarity corresponding to each land type are comprehensively analyzed to obtain the matching similarity of each land type; The land type corresponding to the largest land type matching similarity is the land type corresponding to the grid unit.

9. The GIS-based spatial planning processing system according to claim 1, characterized in that: Obtaining a space planning solution includes the following steps: Based on the thermal map of the best sub-area to be developed, key information data of the best sub-area to be developed are obtained, including heat distribution data, land type ratio data, and heat gradient data; Compare the key information data with the reference key information data of each reference sub-region to be developed stored in the database to obtain the similarity of information of each region; Reference key information data include reference heat distribution data, reference land type ratio data, and reference heat gradient data; Determine the maximum regional information similarity, and record the reference key information data corresponding to the maximum regional information similarity as the reference key information data closest to the key information data; A corresponding reference space planning scheme is obtained from a database based on the reference key information data that is closest to the key information data.

10. A GIS-based spatial planning processing system according to claim 9, characterized in that: Obtaining the similarity of information in each region includes the following steps: Compare the heat distribution data with the reference heat distribution data of each reference sub-region to be developed to obtain the similarity of each heat distribution; Compare the land type ratio data with the reference land type ratio data of each reference sub-area to be developed to obtain the similarity of each land type ratio; Compare the heat gradient data with the reference heat gradient data of each reference sub-region to be developed to obtain the similarity of each heat gradient; The similarity of heat distribution, land type proportion and heat gradient corresponding to each reference sub-region to be developed is comprehensively analyzed to obtain the information similarity of each region.

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